{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Classifier(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "\n",
    "        # activate_func = nn.Sigmoid()\n",
    "        activate_func = nn.LeakyReLU(0.02)\n",
    "        \n",
    "        self.model = nn.Sequential(\n",
    "            nn.Linear(784, 100),\n",
    "            activate_func,\n",
    "            nn.Linear(100, 10),\n",
    "            activate_func,\n",
    "        )\n",
    "\n",
    "        self.loss_function = nn.MSELoss()\n",
    "        # self.loss_function = nn.BCELoss()\n",
    "\n",
    "        self.optimiser = torch.optim.SGD(self.parameters(), lr=0.02)\n",
    "\n",
    "        # record progress\n",
    "        self.counter = 0\n",
    "        self.progress = []\n",
    "\n",
    "    def forward(self, inputs):\n",
    "        return self.model(inputs)\n",
    "\n",
    "    def train(self, inputs, targets):\n",
    "        outputs = self.forward(inputs)\n",
    "        loss = self.loss_function(outputs, targets)\n",
    "\n",
    "        self.optimiser.zero_grad() # set gradient to 0\n",
    "        loss.backward() # backward loss and calculate gradient\n",
    "        self.optimiser.step() # update parameters\n",
    "\n",
    "        # record progress\n",
    "        self.counter += 1\n",
    "        if self.counter % 10 == 0:\n",
    "            self.progress.append(loss.item())\n",
    "        if self.counter % 10000 == 0:\n",
    "            print(f'counter: {self.counter}')\n",
    "\n",
    "    def plot_progress(self):\n",
    "        df = pd.DataFrame(self.progress, columns=['loss'])\n",
    "        df.plot(ylim=(0, 1.0), figsize=(18, 8), alpha=0.1, marker='.', grid=True, yticks=(0, 0.25, 0.5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.utils.data import Dataset\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "class MnistDataset(Dataset):\n",
    "    def __init__(self, csv_file):\n",
    "        self.data_df = pd.read_csv(csv_file, header=None)\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.data_df)\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        label = self.data_df.iloc[index, 0]\n",
    "        target = torch.zeros(10)\n",
    "        target[label] = 1.0\n",
    "        img_values = torch.FloatTensor(self.data_df.iloc[index, 1:].values) / 255.0\n",
    "        return label, img_values, target\n",
    "\n",
    "    def plot_image(self, index):\n",
    "        arr = self.data_df.iloc[index, 1:].values.reshape(28, 28)\n",
    "        plt.title(f'label = {self.data_df.iloc[index, 0]}')\n",
    "        plt.imshow(arr, interpolation='none', cmap='Blues')\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "mnist_dataset = MnistDataset('mnist_dataset/mnist_train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mnist_dataset.plot_image(9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training epoch: 0\n",
      "counter: 10000\n",
      "counter: 20000\n",
      "counter: 30000\n",
      "counter: 40000\n",
      "counter: 50000\n",
      "counter: 60000\n",
      "training epoch: 1\n",
      "counter: 70000\n",
      "counter: 80000\n",
      "counter: 90000\n",
      "counter: 100000\n",
      "counter: 110000\n",
      "counter: 120000\n",
      "training epoch: 2\n",
      "counter: 130000\n",
      "counter: 140000\n",
      "counter: 150000\n",
      "counter: 160000\n",
      "counter: 170000\n",
      "counter: 180000\n",
      "Wall time: 3min 8s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "epochs = 3\n",
    "C = Classifier()\n",
    "for i in range(epochs):\n",
    "    print(f'training epoch: {i}')\n",
    "    for label, image_data_tensor, target_tensor in mnist_dataset:\n",
    "        C.train(image_data_tensor, target_tensor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1296x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "C.plot_progress()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "mnist_test_data = MnistDataset('mnist_dataset/mnist_test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "record = 43\n",
    "mnist_test_data.plot_image(record)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "image_data = mnist_test_data[record][1]\n",
    "output = C.forward(image_data)\n",
    "pd.DataFrame(output.detach().numpy()).plot(kind='bar', legend=False, ylim=(0,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9704 10000 0.9704\n"
     ]
    }
   ],
   "source": [
    "score = 0\n",
    "total = 0\n",
    "for label, image_data_tensor, target_tensor in mnist_test_data:\n",
    "    answer = C.forward(image_data_tensor).detach().numpy()\n",
    "    if answer.argmax() == label:\n",
    "        score += 1\n",
    "    total += 1\n",
    "print(score, total, score/total)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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